The effects of metal artifact reduction on the retrieval of attenuation values

Abstract Background The quality of CT slices can be drastically reduced in the presence of high‐density objects such as metal implants within the patients’ body due to the occurrence of streaking artifacts. Consequently, a delineation of anatomical structures might not be possible, which strongly influences clinical examination. Purpose The aim of the study is to clinically evaluate the retrieval of attenuation values and structures by the recently proposed Augmented Likelihood Image Reconstruction (ALIR) and linear interpolation in the presence of metal artifacts. Material and Methods A commercially available phantom was equipped with two steel inserts. At a position between the metal rods, which shows severe streaking artifacts, different human tissue‐equivalent inserts are alternately mounted. Using a single‐source computer tomograph, raw data with and without metal rods are acquired for each insert. Images are reconstructed using the ALIR algorithm and a filtered back projection with and without linear interpolation. Mean and standard deviation are compared for a region of interest in the ALIR reconstructions, linear interpolation results, uncorrected images with metal rods, and the images without metal rods, which are used as a reference. Furthermore, the reconstructed shape of the inserts is analyzed by comparing different profiles of the image. Results The measured mean and standard deviation values show that for all tissue classes, the metal artifacts could be reduced using the ALIR algorithm and the linear interpolation. Furthermore, the HU values for the different classes could be retrieved with errors below the standard deviation in the reference image. An evaluation of the shape of the inserts shows that the reconstructed object fits the shape of the insert accurately after metal artifact correction. Moreover, the evaluation shows a drop in the standard deviation for the ALIR reconstructed images compared to the reference images while reducing artifacts and keeping the shape of the inserts, which indicates a noise reduction ability of the ALIR algorithm. Conclusion HU values, which are distorted by metal artifacts, can be retrieved accurately with the ALIR algorithm and the linear interpolation approach. After metal artifact correction, structures, which are not perceptible in the original images due to streaking artifacts, are reconstructed correctly within the image using the ALIR algorithm. Furthermore, the ALIR produced images with a reduced noise level compared to reference images and artifact images. Linear interpolation results in a distortion of the investigated shapes and features remaining streaking artifacts.

metal artifact correction, structures, which are not perceptible in the original images due to streaking artifacts, are reconstructed correctly within the image using the ALIR algorithm. Furthermore, the ALIR produced images with a reduced noise level compared to reference images and artifact images. Linear interpolation results in a distortion of the investigated shapes and features remaining streaking artifacts. Furthermore, qualitatively and quantitatively sufficient imaging is required for the differentiation and segmentation of regions being treated and organs at risk, which should be spared. Unfortunately, the image quality of reconstructed CT slices can be reduced by the occurrence of different artifacts. 3 One of the main sources for artifacts is the presence of objects with a high density, that is, prostheses, dental implants, or surgical tools. 3,4 Due to various physical effects such as scatter, beam hardening, noise, or total absorption, projections that pass through such an object can become useless for the reconstruction of the scanned object. This leads to incorrectly reproduced HU values, which in turn, affect the dose calculation. 5,6 Image quality is potentially being reduced up to a point where a delineation of anatomical structures is no longer possible. This drastically influences the clinical examination. 5,[7][8][9] Consequently, an accurate contouring of target structures and organs at risk is no longer guaranteed and the dose planning process is inaccurate.
The correction of metal artifacts remains a highly active field with many different approaches being published every year. 10 However, since publication of the linear interpolation (LI) approach in 1987, only a few advanced methods with a high clinical potential have been proposed. 8,[11][12][13][14] One particular method of interest is the Augmented Likelihood Image Reconstruction (ALIR) that has proven to outperform current methods for clinically relevant data. 15 In order to integrate such a method in the daily routine within a clinical environment, the method needs to be evaluated extensively. 5,7,16,17 Such evaluation should not only focus on retrieving missing anatomical information and improving image quality, but should also investigate the retrieval of correct HU values. Studies that evaluate the performance of MAR methods such as iMAR or VME used tissue-equivalent inserts in phantoms in order to study the HU value retrieval. 7,18 Comparisons of the MAR methods with undisturbed reference images showed that the original HU values could be approximated. In most of the cases, the noise was reduced, while in other cases, the noise was also partly increased. 7,18 Evaluation of the MAR algorithms with respect to their correction capabilities of HU values was slightly limited due to the fact that examined inserts were not alternately positioned on the position with the highest amount of distortion. Therefore, the degree of artifact severity differs for each insert. For a meaningful evaluation, the amount of artifacts should be approximately the same for each insert, which can only be achieved if each insert is positioned at the same location with respect to the metal objects.
Since the ALIR algorithm has already been applied to patient data and has proven that anatomical details can be reconstructed accurately within a complex evaluation in cooperation with radiologists, the algorithm is intensively studied with a focus on the correct retrieval of attenuation values. Here, a commercially available phantom, which is utilized for clinical calibration, is used in order to evaluate the performance of the ALIR algorithm and the LI approach. Different tissue-equivalent inserts are mounted between two metal rods and the reconstructed HU values are analyzed before and after metal artifact reduction. All values are compared to reference images that are acquired without metal rods. Furthermore, the retrieval of the shape of the inserts is analyzed based on the profile plots and a comparison with reference images. Since the present study is limited to phantom data, the reader is referenced to the expensive evaluation of the ALIR algorithm on clinical data in. 15 2 | MATERIAL AN D METHODS

2.C | Reduction of artifacts
For the reduction of metal artifacts, LI and the recently proposed ALIR algorithm is used. 11,15 The ALIR algorithm is based on an iterative scheme and integrates two different ideas in order to reduce streaking artifacts. The reconstruction of an image is modeled as an optimization problem, which utilizes the negative log-likelihood function for transmission CT as the objective. 3 In addition to the objective, the algorithm integrates constraints that force the reconstruction to assign certain attenuation values in the region of the metal implant.
These could be either known attenuation values of the metal implant, which could be gained by utilizing a computer-aided design (CAD) description of the implant, or arbitrary values defined by the user. In the present case, the attenuation value for water is used for the location of the metal object.
The second approach for the reduction of streaking artifacts, which is integrated in ALIR, is based on the interim results of the reconstruction. Let f (k) be the image that can be obtained in the kth iteration. Temporarily appearing artifacts are reduced by applying a bilateral filter to the image f (k) . 19 The filter has two parameters, a geometric spread, r d , and a photometric spread, r r , which can both be adjusted to the manifestation of the artifacts. However   observed. However, some artifacts around the metal objects are remaining. Table 1 shows the mean values, l, and the standard deviation, r, of the analyzed ROI for the reference images, the uncorrected images, the LI results and the ALIR reconstructed images. Furthermore, while the standard deviation for the images with metal artifacts are very high due to the amplified noise and pronounced streaking artifacts, the standard deviation for all tissue classes after metal artifact reduction is smaller than the reference values. This indicates that not only a reduction in streaking artifacts could be gained but also a reduction in the noise level is achieved T A B L E 1 Mean values, l, for all tissue-equivalent inserts at position AI for the reference, metal artifact corrected and not corrected images. Furthermore, the standard deviation, r, is shown for all images. In order to investigate whether the shape of the inserts remains unchanged compared to the reference image, a profile plot of the inserts is examined. Fig. 4 shows the profile of a ROI that features lung 300 and CaCO 3 equivalent tissue at position 5 and 6, respectively. The profile for the image with artifacts features an offset of approx. 300 HU, which is caused by the pronounced streaking artifacts. Furthermore, the perception of edges between phantom body and inserts is heavily affected by streaking artifacts and amplified noise (see standard deviation in Table 1

4.A | Retrieval of HU values
The

4.C | Reduction of noise
The reduced standard deviations after metal artifacts reduction, which are shown in Table 1, indicate a noise reduction in the images.
For ALIR, this behavior can again be explained by means of the bilateral filter used in the algorithm. Controlled by the geometric and photometric spread, a smoothing step is specified for image f (k) in iteration k. The filter does not only reduce streaking artifacts but also smooths homogeneous areas where the attenuation values are within the photometric spread. Therefore, noise is reduced in the filtered image g (k) and transported further in the newly calculated projection values that are used in the reconstruction for iteration k + 1.
The LI approach also results in a reduction of noise but with the important drawback of an overall smoothing of the image, which is generally not desirable. Compared to ALIR, edges of inserts are smoothed and show occasionally a very gradual transition as can be seen in Fig. 4.

| CONCLUSION
An evaluation of HU values that are distorted by metal artifacts is presented and investigated. The results show that it is possible to retrieve these values accurately with the ALIR algorithm. After metal artifact correction with ALIR, structures, which are not perceptible in the original images due to streaking artifacts, are reconstructed correctly within the image. Furthermore, ALIR results in images with a reduced standard deviation compared to the reference and artifact images.
This indicates a promising noise reduction ability of the recently proposed algorithm and will be researched intensively in the near future.
The LI approach on the other hand results in a reasonable retrieval of HU values. However, images show an overall smooth appearance of structures, while the reduction of streaking artifacts is inferior compared to the ALIR algorithm.

CONF LICT OF I NTEREST
The authors state no conflict of interest.